How to Avoid the Ditch When Trying to Find And Develop Oil And Gas Internationally
Bibliographic record
Abstract
Abstract As countries open their hydrocarbon industries to foreign investment, ever-greater numbers of oil and gas companies are adding an international component to their portfolios. Moving outside the well-known realm of domestic operations, foreign operators are exposed to unfamiliar, and at times unforeseen risks. A survey of Canadian managers and executives identifies both the risks and the strategies used to manage the risks in their foreign operations, and is supplemented by an extensive literature review and detailed case study. Approaches to risk management are summarised in the realms of: project selection and data access; political, economic and business risks; NOC/government relationships and cultural differences; project leadership and organisation; EH&S and security risks; technical risk; and senior management support. Introduction The allure for Canadian-based oil companies to expand their exploration and development activities into the international arena is easy to understand-the Western Canadian basin is a fiercely competitive environment, where the conventional oil and gas pools being discovered continue to get smaller. Most hydrocarbon regions of the world are much less explored, and offer the potential for new and larger conventional opportunities, where the advanced technologies to access the difficult reservoirs in Western Canada can be applied. Creating petroleum production internationally through exploration and development is a significant undertaking. The international dimension introduces risks that are not present in a domestic venture. The greatestdifferences when going from a domestic to an international project are:information risk-greater difficulty accessing data before committing to a project;unfamiliar political and business risks;security risks, on occasion;regulatory and partner risk-working with National Oil Companies and foreign governments across language barriers and cultural barriers-both business and social culture;risk of insufficient or diminishing project support-as there is typically less management and investor familiarity with the challenges; andorganizational risk-coordinating project teams across vast distances-and often time zones, with increased project complexity These projects are highly complex, integrating significant technical, business, political, cultural, and organizational challenges. Changing the project location from domestic to international introduces dimensions of risk that will be unfamiliar to project managers of domestic ventures. Important decisions can be made by default by the investor or manager when the risks are not fully understood. It has been my observation that international ventures often fail for reasons that were not anticipated by the participants, but could have been. KEY INFORMATION SOURCES The knowledge assembled in this paper comes from:interviews with expert practitioners in the industryan extensive literature survey?extensive documented experience in a single project (Tinrhert Algeria)the generous sharing of wisdom by colleagues This is a paper on risk management. If you have an academic interest, or desire additional detail, please send me an e-mail (dilger@petro-canada.ca) and I will send the master's thesis on which this paper is based. The referenced literature comprising the thesis extends to about 170 items, however a few of my most useful sources are also referenced at the end of this paper.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.015 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".